A Product Life Cycle Ontology for AdditiveManufacturing

Munira Mohd Alia,∗, Rahul Raia, J. Neil Otteb, Barry Smithb

aDepartment of Mechanical and Aerospace Engineering, State University of New York atBuffalo, Buffalo, NY, USA

bNational Center for Ontological Research, State University of New York at Buffalo, Buffalo,NY, USA

Abstract

The manufacturing industry is evolving rapidly, becoming more complex, more

interconnected, and more geographically distributed. Competitive pressure and

diversity of consumer demand are driving manufacturing companies to rely more

and more on improved knowledge management practices. As a result, multiple

software systems are being created to support the integration of data across

the product life cycle. Unfortunately, these systems manifest a low degree of

interoperability, and this creates problems, for instance when different enterprises

or different branches of an enterprise interact. Common ontologies (consensus-

based controlled vocabularies) have proved themselves in various domains as a

valuable tool for solving such problems. In this paper, we present a consensus-

based Additive Manufacturing Ontology (AMO) and illustrate its application in

promoting re-usability in the field of dentistry product manufacturing.

Keywords: additive manufacturing ontology, manufacturing process

ontology, ontology engineering, product life cycle, dentistry product

manufacturing

IPreprint version of article published in Journal of Computers in Industry, Volume 105,February 2019, Pages 191-203. (https://doi.org/10.1016/j.compind.2018.12.007) .

IIOnly to be used for personal use, copyright belongs to the authors and Elsevier Science.∗Corresponding authorEmail addresses: munirabi@buffalo.edu (Munira Mohd Ali), rahulrai@buffalo.edu

(Rahul Rai), jeffotte@buffalo.edu (J. Neil Otte), phismith@buffalo.edu (Barry Smith)

Preprint version of paper published in Computers in Industry, 105 (2019), 191-203https://doi.org/10.1016/j.compind.2018.12.007

1. Introduction

The economic model of the manufacturing industry is increasingly based on

the modularization of industrial processes and digitally mediated collaboration

between modules both internally, within the enterprise, and externally, through

subcontracting and off-shoring. The sharing of information is crucial for facilitat-5

ing such collaboration across all phases in the life of a product from development

and design, through production and sale, to use and disposal [1, 2, 3]. As

industrial processes come to be further modularized and distributed throughout

the entirety of the manufacturing pipeline, more powerful and more intelligent

software solutions are required to support the different components and phases10

of the product life cycle (PLC).

The Economist Intelligence Unit [4] reports that the need for knowledge

representation of manufacturing processes is increasing exponentially as tech-

nology expedites the rapid exchange of information. In information science, an

ontology is a controlled vocabulary implemented in a semantic or knowledge15

representation language such as the Web Ontology Language (OWL). Ontologies

have been successfully used, for example, in military domains and biomedicine.

In the design and manufacturing domains, in contrast, the use of ontologies

has not lived up to initial expectations, due not least to a lack of coordination

among industrial enterprises. The focus of this paper is to present the Additive20

Manufacturing Ontology (AMO), an ontology designed to represent the Additive

Manufacturing (AM) Product Life Cycle.

AMO is a modular ontology that employs Basic Formal Ontology as its

top level, while also drawing from the Common Core Ontologies and three

other ontologies from the Coordinated Holistic Alignment of Manufacturing25

Process Ontologies that represent manufacturing processes: the Manufacturing

2

Process Ontology, the Design Ontology, and the Testing Process Ontology. As an

illustrative example, the use of AMO is illustrated in the dentistry manufacturing

domain.

2. Related Work30

2.1. Ontology Development in the Manufacturing Domain

Many manufacturing ontologies have been developed in recent years. For

example, Lemaignan et al. [5] developed the Manufacturings Semantics Ontology

(MASON), which employs three top-level classes of entities, operations, and

resources. Entities in MASON comprise a broad class including geometric entities35

(for example, shape), raw materials, and costs. Operations class attempts to

cover all processes involved in manufacturing, and resources attempt to represent

tools, human resources, and geographic resources. MASON was developed as an

upper-level ontology to accomplish two goals:

1. Developing an architecture and tools for automatic cost estimation, and40

2. Linking a high-level ontology written in OWL with a multi-agent framework

for manufacturing simulation.

Unfortunately, MASON’s tripartite division of classes into entities, operations,

and resources are lack of classificatory coherence. Entity in MASON, for instance,

is introduced as comprising the common helper concepts used to specify a product.45

However, entity as defined in the OWL Web Language Guide [6] and OWL2

Web Ontology Language Structural Specification [7] does not limit the definition

in specific concept of specifying product only but also include classes, datatypes,

object properties, data properties, annotation properties, and named individuals

are entities.50

Kjellberg et al. [8] introduces the Machine-Tool Model (MTM) as an ontology

focusing on the machine tool as a central part of a manufacturing system as

3

well as on the way machine tool information is used throughout the design and

operation of such systems. Process planning, for instance, requires information

on the functional properties of machine tools, such as the ability to perform55

different types of machining operations.

Both MASON and the MTM were developed from scratch, each in its ad hoc

way, and they do not use a common upper-level ontology nor do they reuse the

content of other domain ontologies. In this way, they re-create the very lack of

interoperability that they were designed to address, but now this lack occurs60

between ontologies rather than between data systems. Even though MASON was

intentionally developed as an upper-level ontology to represent manufacturing

information, the entities in MASON are identifiable and concrete to represent

manufacturing as the specialized domain of interest instead of being an ontology

that is domain neutral. According to Musen [9], upper-level ontology is defined65

as an ontology at a sufficiently high level of abstraction such that it does not

refer to identifiable, concrete entities in the domain of interest.

MASON’s contribution as an upper-level ontology is undeniably has con-

tributed to the development of other ontologies in manufacturing domain. For

instance, Ramos [10] introduces the Machine Ontology (MO), which elaborates70

the representation of machines in terms of the market, material, and operation

features. The resultant redundancy between MASON, MTM, and MO led Ramos

et al. [11] to present a method for integrating ontology reuse with ontology vali-

dation, and they applied this method to the three ontologies in question, using

Protege-Prompt to find common content and overlapping terms between them.75

The Machine of a Process Ontology (MOP) was developed as a result of this work

with the goal of facilitating the buying and selling of industrial machinery; it

employs MASON as its reference ontology while drawing in relevant classes and

relations from MTM and MO. The CDM-Core Ontology, presented by Mazzola

4

et al. [12] is another ontology that was developed by reusing MASON as one80

of the upper-level ontology. CDM-Core Ontology includes both the general

manufacturing domain applicability and the specific project use cases that can

be a guidance for developing other specific applications in manufacturing domain.

Another ontology, the Manufacturing Service Description Language (MSDL),

was introduced by Ameri et al. [13], who employ a methodology relying on85

the incremental enhancement of an initial set of definitions constructed on the

basis of a formal ontology. MSDL is an upper-level ontology that supports

the semantic framework for representing conventional manufacturing processes

outlined in Kjellberg et al. [8]. The original purpose of MSDL was to serve as

the ontology in an agent-based framework for supply chain deployment; for this90

reason, it employs an analysis of manufacturing capabilities across several levels:

the supplier, shop, machine, device, and process.

Mesmer and Olewnik [14] proposes a Part-Focused Manufacturing Process

Ontology (PMPO) designed around the idea that a classification of manufacturing

processes can be developed on the basis of an account of the desired features and95

attributes of the products they will be used to manufacture. The ontology thus

develops a representation of the qualities used in specifying product requirements,

including material composition, cost, shape, size, the surface finish of the product,

thickness, and so forth. Users can describe the features and attributes based on

the qualities defined in PMPO, and select appropriate manufacturing processes100

according to the information provided.

Most ontologies designed for the manufacturing domain thus far have been

put together with a focus narrowly directed to some specific sub-domain of

manufacturing engineering and with little attention to interoperability with

other ontologies in related domains. Among the ontologies discussed, MOP105

and PMPO stand out because they build on prior work. PMPO is especially

5

interesting in that it utilizes not only Basic Formal Ontology but also MSDL,

the Ontology for Biomedical Investigations (OBI), and the Common Semantic

Model Ontology (COSMO). It is thus, at least to some degree, able to achieve

interoperability among data systems deriving from external sources. On the110

other hand, PMPO is small in scale and has been designed only for traditional

machining and molding processes thus it cannot be applied to more modern

manufacturing processes such as additive manufacturing.

Ideally, a representation of the manufacturing domain should deal with

commonly collected product-related information. Moving forward, we hold115

that an ontological representation of products and the PLC is a prerequisite

for integrating data across systems in the manufacturing domain. Therefore,

developing an ontology with a focus on AM products - their qualities, functions,

the production, use, and end-of-life is the main objective of this paper. However,

our ontology is intended to form part of a larger suite of modular ontologies120

within the framework of the Industrial Ontologies Foundry (IOF) initiative, and

it will accordingly be modified in tandem with IOF development. IOF is an

initiative that was proposed to promote interoperability of high quality and

non-redundant ontologies in industrial domains or manufacturing specializations

[15].125

2.2. Manufacturing Processes and the Product Life Cycle (PLC)

As customer demands diversify, the complexity of products and product

repertoires increases, and this gives rise to demand for increasingly innovative

manufacturing processes [16]. Understanding the nature of such processes and

creating computational systems that can understand and reason about them is130

crucial, and this means understanding and reasoning across the entire product

life cycle (PLC) in the Product Life Cycle Management (PLM). Moreover,

Product, Process and Resource (PPR) are the key elements of engineering

6

domain in any manufacturing industry [17]. The information about PPR that

is structured in PLM systems requires explicit mapping among the PPR for135

a complex decision purpose. Therefore, an ontology that provides common

vocabularies in representing knowledge could facilitate the full potential of the

PLM by supporting information exchange between the PPR in different phases

in PLC[17, 18].

Cao and Folan [19] divide PLC models into two groups:140

1. Marketing Product Life Cycle (M-PLC) models that focus primarily upon

marketing needs and conceptions;

2. Engineering Product Life Cycle (E-PLC) models that integrate design and

manufacturing with marketing needs and conceptions.

Figure 1 represents the successive phases of the PLC taken as the basis of145

ontology development in many recent works, including Young et al. [3], Chen

et al. [16], Borsato [18], Matsokis and Kiritsis [20], Chungoora et al. [21], Usman

et al. [22], Urwin [23], Urwin et al. [24], and others.

Figure 1: Phases in the PLC.

Table 1, from Chen et al. [16], extends this representation to create a more

granular perspective. Here, the PLC is depicted as consisting of seven stages150

each with a number of sub-stages.

Chen et al. [16], Borsato [18], Matsokis and Kiritsis [20], Chungoora et al.

[21], Usman et al. [22], Urwin [23] and Urwin et al. [24] have demonstrated

good concept of information integration and sharing between the design and

manufacturing phases in PLC through ontology. Borsato [18] for instance,155

7

Table 1: Seven stages of the PLC presented by Chen et al. [16]

Stages Sub-stagesProduct design Requirement analysis, Conceptual design, Prelimi-

nary design, Detail designProcess development Part description, Generative process planning, Vari-

ant process planningProduct manufacturing Equipment layout, Production management, Qual-

ity controlSales Chance analysis, Target market choice, Sell combi-

nation developmentProduct in use Operation instructions establishment, Product in-

stallation and executionPost sell service User problem reaction, Problem identification, Ser-

vice supportProduct retirement Decomposition, Recycling

believes that ontology could bridge the gap between manufacturing and PLC.

Chungoora et al. [21] presents the Interoperable Manufacturing Knowledge

System (IMKS) model-driven concept that was built on the ideas of extensible

core ontologies of manufacturing. Usman et al. [22] forms the Manufacturing

Core Concepts Ontology (MCCO) by identifying core set of concepts formalized160

in the upper-level ontology that serve as foundation ontology to provide the first

stage of a common understanding before developing the domain specific concepts

of design and production.

In what follows, we will adopt this granular perspective in conceiving of

the PLC as having a scope that includes processes of design and development,165

manufacturing, usage, maintenance and disposal, as well as the information,

materials, qualities, and functions that participate in these processes.

In particular, we take into account also the following key areas of interest

regarding the PLC identified by Young et al. [3]:

� Information regarding products including product geometry,170

� Potential supply chain capability,

8

� Knowledge of what has been done in the past, and

� Potential legislation, catalog data, and standards that affect decision

making.

2.3. Previous Work on Additive Manufacturing Ontology175

Additive manufacturing (AM) is defined by the American Society for Testing

and Materials (ASTM) as: the process of joining materials to make objects from

3D model data, usually layer upon layer, as opposed to subtractive manufac-

turing methodologies [25]. AM is nowadays widely used in industrial product

development, and its ability to create almost any possible shape through a180

process of building up a product layer by layer.

Existing work on the ontology for AM includes SAMPro, for Semantic

Additive Manufacturing Process Planning, described by Eddy et al. [26]. SAMPro

is extended from the MSDL; it provides the starting point for a module that

includes types of AM such as the Binder Jetting and Directed Energy Deposition185

as its classes and focuses on the products which are the output of such processes,

representing in detail product features such as surface finish, accuracy, tolerance,

and so forth.

By contrast, The Design for Additive Manufacturing Ontology (DFAM),

presented in Dinar and Rosen [27], focuses on the detailed representation of190

different types of AM processes in terms of what it calls process parameters such

as printing orientation angle. However, DFAM, too, suffers from the fact that it

has been developed with its own peculiar vocabulary for the included classes.

NIST [28], Roh [29] and Liang [30] are some other ontologies for AM that

have been developed recently. Roh [29] develops an ontology for AM to represent195

information for different process models for laser, thermal, micro structure, and

mechanical properties for metal-based AM of Ti-6-6Al-4V. Liang [30] develops

the AM-OntoProc ontology that promote the modeling and reutilization of

9

knowledge towards the AM process planning where AM process is supposed to

begin from the utilization of CAD software during the design stage until the200

final AM prototype is developed.

Finally, there is recent work by Hagedorn et al. [31], which uses BFO as

the platform for an AM ontology called Innovative Capabilities of Additive

Manufacturing (ICAM). ICAM also reuses the BFO-conformant ontology - the

Information Artifact Ontology (IAO) - to provide the higher-level representation205

of information-related types that serves as its backbone. The information in

ICAM covers basic product attributes from the NIST Core Product Model

(CPM) relating to materials, geometry and designed function as well as types of

manufacturing processes and services taken over from MSDL. It also incorporates

the SAMPro model of AM and a set of formal description of parts and features210

from Functional Basis Ontology (FBO). ICAM is thus able to provide extensive

coverage of the AM domain and since our version of AMO also reuses the BFO-

conformant ontology - the Common Core Ontologies (CCO), we will be looking

forward for the opportunity to develop future versions of AMO to be consistent

with the ICAM content that focus on the application of AM. The CCO is a215

suite of ontologies that was released to the public recently that adds general

contents to the BFO structure and at the same time are also common to many

domain of interest, especially to manufacturing engineering domain. We feel

that Information Entity Ontology (IEO) that is part of CCO seems to be able to

represent information-related types to manufacturing domain in a more accurate220

way than IAO. However, there are still works to be done in making the IEO and

IAO to be compatible to each other.

10

3. Approach for Ontology Development

Although there are is no standard methodology for developing ontologies,

Natalya F and Deborah L [32] outlined a simple knowledge-engineering method-225

ology in developing ontology which was followed as a guideline in our work. The

methodology include: determining ontology domain and scope, considering on-

tology reuse, enumerating important terms, defining classes and class hierarchies,

defining class properties, defining values for properties, and creating instances of

classes.230

We adopted a top-down approach in most of the ontology development

process where the AMO as the domain ontology was constructed by downward

population from a common upper-level ontology in the multi-tiered network

connected in the following way [33]:

1. A single, small, domain-neutral upper-level ontology;235

2. Mid-level ontologies covering broad domains having root nodes that are

either direct children of classes from the upper-level ontology or of a term

drawn from another mid-level ontology within the network;

3. Lower-level ontologies representing specialized domains having root nodes

that are either direct children of classes from one of the mid-level ontologies240

or of a term drawn from another domain level ontology within the network;.

BFO and Descriptive Ontology for Linguistic and Cognitive Engineering

(DOLCE) are some of the upper-level ontology that have been used as the

foundation ontologies in the domain ontology development [34, 35, 36]. Both

ontologies in fact grew out of a common philosophical orientation, and thus some245

parts of the ontologies overlapped with each other. Since our work is part of the

Coordinated Holistic Alignment of Manufacturing Processes (CHAMP) project

founded by Digital Manufacturing and Design Institute (DMDII) which focuses

on constructing an efficient scheme to manage discordant manufacturing data

11

source, BFO is already selected as one of the project requirement. However,250

despite of the selection of BFO due to the CHAMP project requirement, BFO’s

well-documented guidelines and training material, it’s extensive use in in hundreds

of projects in biomedical and military domains, and increasingly being adopted

in industry as a top-level framework are another factors that contribute to the

selection of BFO. ICAM [31], CCO [37] and Functional Graded Material Ontology255

(FGMO)[38] are some of the ontologies that have been developed by adopting

BFO as foundation of the ontology development. These factors make its use a

key enabler in promoting the secondary use of our ontology by others.Therefore,

we selected BFO1 as an upper-level ontology to serve as starting point in the

ontology development of AMO. In addition, we have wherever possible reused260

content taken from the Common Core Ontologies (CCO)2 , which were also built

as conservative extensions of BFO.

BFO contains the top class entity that contains two subclasses: continuant

and occurrent [34]. A continuant is an entity that is wholly present at every

time during the course of its existence. Examples of continuant entities include265

objects, such as tables and people, as well as spatial regions and portions of

matter, qualities, such as the length of an airplane wing, and dispositions, such

as the tensile strength of a steel sheet. An occurrent, by contrast, is an entity

that occurs or happens by unfolding itself through time in successive phases (for

example of a beginning, middle, and end). Manufacturing processes fall under270

this heading, as also do the temporal regions during which such processes occur.

A fragment of the BFO class hierarchy is provided in Figure 2.

An AM process is a process that involves certain sorts of material entities

as its participants. A Portion of Material is a subclass of material entity, and

1See https://github.com/BFO-ontology/BFO/blob/master/bfo.owl2See https://github.com/CommonCoreOntology/CommonCoreOntologies

12

Figure 2: A fragment of the BFO class hierarchy [39].

instances of Portion of Material are the inputs for instances of AM process.275

Both processes and material entities are distinct from the third type of

entity in BFO comprising generically dependent continuants. These are, roughly,

patterns that can be exactly copied - they are entities that depend on the

existence of at least one bearer at any time during which they exist, but not on

any particular bearer. The most important subclass of generically dependent280

continuant is Information Content Entity, whose instances stand in a relation of

aboutness to some entity [40]. Importantly, for our purposes, this class includes

instances of reports, sentences, and data values that are about the processes and

materials of a manufacturing process. Such information is not itself material,

nor is it a process, though it may participate in processes [34].285

The Common Core Ontologies (CCO) form a set of conservative extensions

of BFO with the goal of representing the mid-level entities involving agents,

artifacts, actions, and measurements [41]. The ontologies in the CCO include:

13

� Agent Ontology, representing agents, especially persons and organizations,

and their roles.290

� Artifact Ontology, representing deliberately created material entities along

with their models, specifications, and functions.

� Currency Unit Ontology, representing currencies in different countries.

� Extended Relation Ontology, representing relations (i.e. object properties)

holding between entities.295

� Event Ontology, representing processes.

� Geospatial Ontology, representing sites, spatial regions, and other entities,

especially those that are located near the surface of Earth, as well as the

relations that hold between them.

� Information Entity Ontology, representing generic types of information as300

well as the relationships between information and other entities.

� Quality Ontology, representing a range of attributes of entities, including

qualities, realizable entities such as dispositions and roles, and process

profiles.

� Time Ontology representing temporal regions and the relations that hold305

between them. A temporal region, as defined by BFO, is an occurrent

entity that is part of time as defined relative to some reference frame.

� Units of Measure Ontology, representing standard units used when mea-

suring various attributes of entities.

Figure 3 shows the CCO ontologies with the import structure between them.310

Every class in CCO is the subclass of some class in BFO, and general relations

used in BFO are also adopted by the CCO.

14

Figure 3: The Common Core Ontology import structure [37].

Although CCO served as the mid-level ontology that reduce the generality

of BFO, having direct children from CCOs’ classes to represent the specific

domain in AM are not sufficient. There is a need of another level of ontology315

after the CCO so that it can increase the granularity of domain before moving

towards the concrete entities that represent AM. The Coordinated Holistic

Alignment of Manufacturing Processes (CHAMP) is a project funded by the

Digital Manufacturing and Design Innovation Institute (DMDII). The CHAMP

project has developed a suite of ontologies whose objective is to aid industrial320

organizations in overcoming the problem of data heterogeneity. The CHAMP3

ontologies are an extension of CCO representing the mid-level classes relating

to the design, manufacturing, use, and maintenance phases of the PLC. These

ontologies include:

3See https://github.com/NCOR-US/CHAMP

15

� Product Life Cycle Ontology325

� Commercial Entities Ontology

� Design Ontology

� Manufacturing Process Ontology

� Testing Process Ontology

� Tool Ontology330

� Maintenance Ontology

Figure 4 shows the CHAMP ontologies and their import structure.

Figure 4: The CHAMP Ontologies

Ideally, we planned to extend the whole set of CHAMP ontologies for the

development of AMO. However, since the CHAMP ontologies are still in initial

implementation, we decided to import only a fragment of the CHAMP ontologies335

that are related to the AM processes. We imported manually some of the

16

classes from the Design Ontology, Manufacturing Process Ontology, and Testing

Process Ontology without changing its’ class structures and URI from its’ original

ontologies. Figure 5 shows the framework for the AMO development.

Figure 5: An overview of AMO development.

4. The Additive Manufacturing Ontology (AMO)340

Existing classes from BFO, CCO, and CHAMP are imported into AMO and

new classes are added in a process of downward population. This ensures that

AMO utilizes commonly used terms and definitions and thereby increases the

chances that AMO will itself be re-used and integrated with other ontologies.

4.1. Process345

There is a canonical order to processes that occur within AM, and that

the processes represented in the AMO were selected in order to account for

this canonical representation. From a mechanical perspective, AM often uses

numerically controlled (NC) machines that are integrated with CAD and process

planning software. The canonical AM process flow consists of six steps [42]:350

1. 3D CAD model generation;

2. Conversion of the CAD Model into AM machine acceptable format (STL

file);

3. Setting the process parameters;

17

4. The process of printing;355

5. Support removal; and

6. Post-processing.

The class process is defined in BFO as an occurrent entity that exists in

time by occurring or happening, has temporal parts, and always depends on

some (at least one) material entity [34]. Each of the steps listed by Yang et al.360

[42] corresponds to the instantiation of a certain type of process entity. Process

entities in AMO include:

1. Design Ontology: ActOfDescribingClientNeed

2. Design Ontology: ActOfAnalysisOfClientNeed

3. AMO: ActOfCADModelDevelopment365

4. AMO: ActOfDataTransformation

5. Design Ontology: ActOfManufacturingRequirementIdentification

6. Manufacturing Process Ontology: ActOfAdditiveManufacturing

7. AMO: ActOfSupportRemoval

8. AMO: ActOfPostProcessing370

As can be seen, some of these classes are imported from the Design Ontology

and the Manufacturing Process Ontology. Figure 6 shows the taxonomy of the

process classes in AMO. IntentionalAct as can be seen from the figure is the

subclass of Act class where both Act and IntentionalAct are the CCO classes,

extension of Process class in BFO. The definition of both class as follows:375

� Act is a process in which at least one agent plays a causative role.

� IntentionalAct is an Act in which at least one agent plays a causative role

and which is prescribed by some Directive Information Content Entity

held by at least one of the Agents.

18

Figure 6: Process classes in AMO.

In addition to the eight main process classes, there are also conditions where380

main process classes at the instance level has other process as part of the main

19

process. However, it is not right to put that process class as the subclass of the

main process since this process part relations only hold in some cases and not

all the time. Figure 7 shows the example of some ActOfSupportRemoval that

has process part an ActOfMaterialRemoval and an ActOfPostProcessing that385

has an ActOfAbrading and an ActOfJoining as process parts.

Figure 7: Example for process parts of Act of Support Removal and Act of Post Processing.

20

4.2. Material Entity

BFO defines a material entity as an independent continuant that has some

portion of matter as part [34]. Three types of material entity are recognized by

BFO: object, fiat object part, and object aggregate [34]. BFO does not assert that390

all material entities fall under one or other of these headings. Thus, portions

of liquid, gas, and plasma are classified (in the current version of BFO) as

immediate descendants of material entity.

Resources involved in the AM process can all be classified as material entities

in the AMO. These resources are:395

� Portion of Material

� 3D Printing Machine

� Printed Object

� Finished Object

Portion of Material in AMO is a direct child of material entity because it400

may describe those material entities that are not object aggregates. Material

entity classes in AMO are shown in Figure 8. Meanwhile, 3DPrintingMachine,

PrintedObject, and FinishedObject are types of object in BFO [43]. PrintedObject

and FinishedObject are defined classes with definitions as follows:

� PrintedObject is an object that is and output of some Act of Additive405

Manufacturing.

� FinishedObject is an object that is an output of some Act of Post-Processing.

4.3. Information Content Entity

An Information Content Entity as defined by the CCO is ”a Generically

Dependent Continuant that generically depends on some Information Bearing410

21

Figure 8: Material Entity classes in AMO.

Entity and stands in the relation of aboutness to some entity4”. The CCO also

provides three sub-relations of ‘is about ’, including: describing, prescribing, and

designating.

The AMO makes use of these distinctions provided in the CCO to provide

a series of classes of information that either prescribe, designate, or describe415

various AMO processes and resources. These are displayed in Figure 9 and newly

added classes appear in bold. CADSoftwareProgram, CADModel, STLDataFile,

and TechnicalDrawing are InformationContentEntity in AMO. Thus, all are

generically dependent continuants in BFO. A CADModel, for example, contains

information pertaining to the qualities that must inhere in a solid model of the420

sort that is represented in an ArtifactModel. An ArtifactModel in the CCO is a

subclass of ArtifactDesign. Both classes are defined in the CCO5 as follows:

� ArtifactDesign is a Directive Information Content Entity that is a specifi-

cation of an object, manifested by an agent, intended to accomplish goals,

in a particular environment, using a set of primitive components, satisfying425

a set of requirements, subject to constraints.

4See https://github.com/CommonCoreOntology/CommonCoreOntologies/blob/master/

AllCoreOntology.ttl5See https://github.com/CommonCoreOntology/CommonCoreOntologies/blob/master/

AllCoreOntology.ttl

22

� ArtifactModel is an Artifact Design that prescribes a common set of

functions and qualities that are to inhere in a set of artifact instances.

Figure 9: Information Content Entity classes in AMO.

23

The CCO class, Directive Information Content Entity is defined as an Infor-

mation Content Entity that prescribes some entity. Specifications, for example,430

are classified under Directive Information Content Entity, as for example where

some Additive Manufacturing Process Specification prescribes some Act of Addi-

tive Manufacturing.

Requirements, too, are classified under Directive Information Content Entity,

for example when a Customer Requirement prescribes certain qualities that must435

inhere in a product. Standards documents such as ISO 3923, the International

Standard for Metallic Powders, prescribes the level of quality that a metallic

product must have if it is to satisfy the standard.

4.4. Relations

Figure 10 provides an overview of the relations used in AMO, in addition to440

process part of discussed in Section 4.1.

Definitions from BFO and the CCO are as follows:

� has participant is a primitive instance-level relation between a process, a

continuant, and a time at which the continuant participates in some way

in the process.445

� is input of is a relation between a continuant and a process in which the

continuant participates. The presence of the continuant at the beginning

of the process is a necessary condition for the initiation of the process.

� is output of is a relation between a continuant and a process in which the

continuant participates. The presence of the continuant at the end of the450

process is a necessary condition for the completion of the process.

� prescribes is for all types T1 and T2, if T1 prescribes T2, then there is

some instance of T1, t1, that serves as a rule or guide to some instance of

T2, t2.

24

Figure 10: An overview of AMO entities and relations.

25

5. Application of AMO to the Dentistry Product Manufacturing455

AMO was developed to serve as a mid-level ontology that can be re-utilized

for multiple different types of additive manufacturing. By providing terms for all

processes within the AM organized process flow, we feel that AMO is particularly

well suited for PLC applications. Besides, to ensure generality of AMO, the

properties interrelating objects of different ontologies are only defined directly460

in the AMO at the instance level. To show the utility of the developed AMO

for developing application-specific ontologies, a case study applying AMO to

dentistry manufacturing application termed Additive Manufacturing for Dental

Product Ontology (AMDO) is discussed next.

5.1. Additive Manufacturing (AM) in Dentistry465

AM has established itself in the dentistry field as a promising alternative to

the conventional manufacturing processes. AM has the advantage of yielding

accurate one-off fabrication of complex structures in a variety of materials

having properties highly desirable for both dentistry and surgery [44]. AM

even becoming a feature of many dental surgeries [45], where it allows direct470

fabrication of dental prostheses such as crowns and bridges.

As a case study, an application ontology extending AMO with new classes

related to the dentistry product manufacturing field has been developed. This

application ontology is titled the Additive Manufacturing for Dental Product

Ontology (AMDO) and is depicted in Figure 11 (newly added classes are high-475

lighted in bold). As can be seen from the figure, the newly added classes are the

extension of the existing classes from the imported ontologies. This shows the

re-usability of AMO in the developing the application ontology. No new object

properties are needed to be created as well.

26

Figure 11: Active classes in AMDO.

5.2. Case Demonstration480

This section demonstrate an example of the practical uses of the AMDO

guided by the work outlined in Khalil et al. [46]. In their work, they evaluate

dimensional differences between natural teeth and the printed models using three

different AM processes. The process starts with the scanning of three premolars

27

dimension from two dry adult human mandibles by means of an optical scanner485

to yield the three-dimensional data needed for the dental model. Table 2 shows

the specifications of the four 3D printers used in the study Khalil et al. [46].

Table 2: Specification of 3D Printers used

Machine SLA Objet Eden 250 Objet Connex 350 UP Plus 2

Printing type SLA Polyjet Polyjet FDMLayer thickness (µm) 50 16 16 150Material used Resin Resin Resin ABS

24 printed premolar tooth were produced in total and the volume of each

replicas are measured and compared against the original premolars. Table 3

shows the overview of the volume measurements of each printed premolar tooth490

with the percentage of volume difference with the original premolars.

Table 3: Volume and Percentage of Volume Differences DataGroup Tooth No. UP Plus 2 Objet Connex 350 Objet Eden 250 SLA

M1 34 509.2(4.2%) 502.1(2.7%) 482.6(-1.3%) 484.2(-0.9%)M1 44 616.7(18.5%) 521.2(0.1%) 516.2(-0.9%) 519.3(-0.3%)M1 54 440.5(-1.6%) 438.8(-2.0%) 446.2(-0.3%) 448.0(0.1%)M2 34 376.5(-4.4%) 421.6(7.0%) 398.2(1.1%) 388.9(-1.3%)M2 44 340.2(-6.8%) 351.0(-3.8%) 361.0(%-1.1%) 362.1(-0.8%)M2 54 416.4(-12%) 464.2(-1.9%) 482.6(%-0.9%) 471.1(-0.4%)

For the testing purposes of AMDO, we populated all data from Table 2 and

Table 3 as instances for the classes in AMDO. Following are the three queries

that we have made for the AMDO to provide the inferred information:

1. What are the 3D Printers used in the study?495

2. What type of materials used for each machine and what are the layer

thickness specification in fabricating the printed tooth?

3. What are volume differences of the printed tooth with the uses of different

3D printers?

The queries have been created using an Resource Description Framework (RDF)500

query language, SPARQL that is a plugin to Protege. SPARQL is a semantic

query language that is able to retrieve and manipulate data stored in the

28

RDF format where the entities and its relations are expressed in the form of

subjectpredicateobject. For each queries, following are the namespace and its

binded prefixes that were used to identify the URI of the classes:505

rdf: http://www.w3.org/1999/02/22-rdf-syntax-ns#

owl: http://www.w3.org/2002/07/owl#

rdfs: http://www.w3.org/2000/01/rdf-schema#

xsd: http://www.w3.org/2001/XMLSchema#

bfo: http://purl.obolibrary.org/obo/510

ros: http://www.obofoundry.org/ro/ro.owl#

ccos: http://www.ontologyrepository.com/CommonCoreOntologies/

plco: http://www.semanticweb.org/no/ontologies/2017/1/PLC-ontology/

mpos: http://www.semanticweb.org/ontologies/ManufacturingProcessOntolog

y/515

dos: http://www.semanticweb.org/no/ontologies/2017/6/DesignOntology/

amos: http://www.semanticweb.org/munira/ontologies/2017/6/AdditiveMan

ufacturingOntology#

amdo: http://www.semanticweb.org/munira/ontologies/2018/1/AdditiveMan

ufacturingDentalOntology#520

Query 1: Identifying 3D printers used in the study.

The SPARQL Query for this question is as follow:

SELECT ?Machine

WHERE {525

?Machine rdf:type amos:3DPrintingMachine }

As can be seen from Table 2, there are four 3D printers used in the study. Even

though there are only four data, this simple query represents those with large

29

input and low selectivity and does not assume any hierarchy information or530

inference. Figure 12 shows the result of this query.

Figure 12: Query 1 Result

Query 2: Identifying type of materials used for each machine and what are the

layer thickness specification in fabricating the printed tooth.

To identify the type of materials used for each machine and with the layer535

thickness specification for the printing process, the relations between the process,

machine, materials and also the process specification are defined at the instance

level. To increase the selectivity of the inferred information, we limited type of

printing process to only to the Vat Photopolymerisation Process. The SPARQL

Query for this question is as follow:540

SELECT ?Machine ?Material ?LayerThickness ?MeasurementUnit

WHERE {

?a rdf:type amos:ActOfVATPhotopolymerisation .

?Machine ros:agent in ?a .

?Machine rdf:type amos:3DPrintingMachine .545

?b ccos:is input of ?a .

?b rdf:type amos:PortionOfMaterial .

?b ccos:has text value ?Material .

?c ccos:prescribes ?a .

?c rdf:type amos:LayerThicknessSpecification .550

?c ccos:inheres in ?d .

?d rdf:type ccos:InformationBearingEntity .

30

?d ccos:has integer value ?LayerThickness .

?d ccos:uses measurement unit ?MeasurementUnit }

This query increases in complexity where there are four classes are involved and555

it has high selectivity due to the contraint added to one of the class. Figure 13

shows the result of this query.

Figure 13: Query 2 Result

Query 3: Identifying volume differences of the printed tooth with the uses of

different 3D printers?560

To identify the volume differences of the printed tooth with the uses of different

3D printers,the relations between the process, machine, and also the volume

measurements with the volume difference analysis data are defined at the in-

stance level. The illustration of the relations between instances of each class is

shown in Figure 14.565

To increase the selectivity of the inferred information, we limited type of printing

process to only to the Vat Photopolymerisation Process. The SPARQL Query

for this question is as follow:

570

SELECT?Machine?ProstheticTooth?ProstheticToothVolume?VolumeDifference

Percentage

WHERE {

?a rdf:type amos:ActOfMaterialJetting .

?Machine ros:agent in ?a .575

31

Figure 14: Instances in AMDO

?Machine rdf:type amos:3DPrintingMachine .

?ProstheticTooth ccos:is output of ?a .

?ProstheticTooth rdf:type amdo:PremolarProstheticTooth .

?ProstheticTooth ccos:bearer of ?d .

?d rdf:type amos:VolumeCapacity .580

?d ccos:is measured by ?e .

?e rdf:type amos:QualityMeasurementInformationContentEntity .

?e ccos:inheres in ?f .

?f rdf:type ccos:InformationBearingEntity .

?f ccos:has decimal value ?ProstheticToothVolume .585

?e ccos:is input of ?g .

?g rdf:type amos:ActOfAnalysisOfInspectionData .

32

?h ccos:is output of ?g .

?h rdf:type amos:AnalysisMeasurementInformationContentEntity .

?h ccos:inheres in ?i .590

?i rdf:type ccos:InformationBearingEntity .

?i ccos:has decimal value ?VolumeDifferencePercentage }

Figure 15 shows the result of this query.

Figure 15: Query 3 Result

The answers to the example of queries show the ability of the ontology to595

retrieve information that matched to the queries even though the classes in

AMDO are imported from different ontologies. This is because, AMDO is an

extension of AMO and AMO is an extension of CHAMP which are extended

from the CCO and BFO. We use the import process in developing the ontology

to maintain the URI of the classes so that the naming of the class, the class600

structure, the class definition and the class relations will be standardized in all

related ontologies. This will ensure re-usability of the ontology. Even though,

we have not tested the interoperability of the ontology yet, but aiming for the

re-usability of the ontology is a starting point in achieving interoperability of

the ontology.605

Nevertheless, due to the import structure of the AMDO with the AMO,CHAMP,

CCO and BFO, we will have CCO terms available in AMDO. The CCO terms

33

may then extend the reach of AMDO to cope with corresponding data concern-

ing persons and organizations, roles of persons (for instance dental technician,

patient), measurement units, and cost factors. Thus, the functionality of AMDO610

can be extended to cope with a digital record or a patients history record in such

a way as to document the process of maintenance of the dental crown, comparing

susceptible of wear of the crown and of associated dental disorders in different

patients, perhaps incorporating also terms from the OBOFoundry6.

6. Conclusion and Future Work615

The AMO was developed within the context of a more general treatment

of the PLC. It will be helpful to users who employ AM in their work, and who

face the challenge of data integration faced by most modern industries today.

It can assist the designer in designing a new product, by enabling access to

bodies of data across the entire dentistry product manufacturing domain, for620

example relating to materials used, patient experiences, maintenance costs, and

so forth. The framework is also sufficiently general that it may accommodate

the generation of more fine-grained application ontologies in other areas where

AM technology is applied.

As the manufacturing industry is evolving rapidly and becoming more com-625

petitive, quality and cost are major factors that need to be focused on by the

manufacturers. These factors are affected closely by the process and the material.

We concentrated here primarily on the process aspect in the AM process but

in the next stage, we will work on integrating the AMO with the ontology that

represents the types of material used in AM and their associated attributes.630

This will build on work on the ontology of material that is part of the CHAMP

ontologies, where each ontology in CHAMP constitutes a mid-level ontology that

6See http://www.obofoundry.org/ontology/ohd.html

34

imports the whole of the CCO, as well as BFO.

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